# Using UniPercept for VPhotoBench Outputs

VPhotoBench provides Blender scenes and text prompts. After running an agent or
camera-search method, users can evaluate the final rendered images with
UniPercept:

https://github.com/thunderbolt215/UniPercept

UniPercept returns three perceptual scores for each image:

- `IAA`: image aesthetics assessment.
- `IQA`: image quality assessment.
- `ISTA`: image structure and texture assessment.

## 1. Prepare Results

Create a CSV file with one row per rendered result:

```csv
task_id,image_path
001_blender_4_0_splash_subject_placement,images/001_blender_4_0_splash_subject_placement.png
```

`task_id` should match the IDs in `benchmark/tasks.json`. `image_path` can be
absolute or relative to the submission/result directory.

## 2. Install UniPercept

Follow the official UniPercept instructions. The lightweight package interface
can be installed with:

```bash
pip install unipercept-reward
```

If you use a local checkpoint or a specific UniPercept release, follow the setup
steps in the UniPercept repository.

## 3. Score Images

Run:

```bash
python evaluation/run_unipercept_scoring.py \
  --benchmark-root . \
  --submission path/to/submission.csv \
  --submission-root path/to/result_directory \
  --output path/to/unipercept_scores.csv \
  --device cuda
```

The output CSV contains:

```text
task_id,scene_id,mission_type,image_path,status,iaa,iqa,ista,m_qs,succ_at_0_55,error
```

The script normalizes UniPercept scores from `0-100` to `0-1`. It also reports
an optional composite score:

```text
M_qs = 0.40 * IAA + 0.20 * IQA + 0.40 * ISTA
```

and:

```text
Succ@0.55 = 1 if M_qs >= 0.55 else 0
```

These two derived fields are provided only as a convenient default summary.
Users may report the three UniPercept scores directly or define their own
aggregation rule for a specific study.